Standard Job Description

Long Description

When it comes to using cutting-edge machine learning to tackle complex problems, Lockheed Martin is driven by a singular mission focus and desire to continuously innovate! Today’s challenges to global security aren’t just changing - they’re accelerating faster than ever before. Through our dedication to our mission, our AI-enabled systems are changing the way militaries operate and protect their forces, the way first responders fight fires, and how researchers explore the far reaches of space and the ocean’s depths.

The Lockheed Martin Artificial Intelligence Center (LAIC) is seeking a high energy Cyber Software Security Engineer to support the AI Factory, an enterprise Artificial Intelligence and Machine Learning (AI/ML) platform. In this role, you will be embedded in the AI Factory team working alongside data engineers, AI/ML engineers, product managers, and data scientists, with a primary focus on Test, Evaluation, Verification, and Validation (TEVV) of AI/ML systems. The selected candidate will help ensure that AI/ML models, pipelines, and platforms deployed across Lockheed Martin are rigorously assessed for security, robustness, reliability, and trustworthiness. Additionally, they will contribute to a broad range of TEVV projects across the enterprise to increase the assurance, availability, and value of artificial intelligence to Lockheed Martin.

The selected candidate will:

• Design, build, and secure TEVV infrastructure and tooling to evaluate machine learning systems that power Lockheed Martin's AI platforms
• Develop and mature AI TEVV practices, standards, and methodologies within Lockheed Martin, including adversarial testing, red-teaming, and model assurance techniques
• Work with AI/ML practitioners to define evaluation criteria, benchmarks, and acceptance thresholds that verify model behavior, safety, and security across the AI lifecycle
• Contribute to robust evaluation infrastructure to measure model capabilities, vulnerabilities, biases, and risks across multiple domains and mission contexts
• Build and scale secure runtime environments and automated pipelines for continuous AI assessment, including reproducible test harnesses, evaluation datasets, and regression testing frameworks
• Evaluate the latest TEVV, security automation, and model assurance packages and frameworks in the ML and data ecosystems to enable trustworthy end-to-end development pipelines
• Investigate AI-specific threats (e.g., adversarial examples, data poisoning, model extraction, prompt injection) and integrate corresponding verification and validation checks into CI/CD workflows

Basic Qualifications

• Experience with programming and scripting languages such as Javascript/Typescript, Python, Go, Rust, and Bash
• Experience with Application Security, Software Architecture and Design, and building or integrating test/evaluation frameworks
• Experience with collaboration tools, such as Teams, Confluence, Gitlab
• Knowledge of Cyber Security concepts (Intelligence Driven Defense, Threat Driven Methodologies, or Cyber Kill-Chain) and their application to AI/ML systems
• Knowledge of Kubernetes (OpenShift, Rancher, GKE, EKS)

• Knowledge of DevSecOps, MLOps, and Cloud Native software development practices with emphasis on continuous testing and validation

• Familiarity with containerized development, including Docker and Open Container Initiative (OCI)

Desired Skills

• Experience with pipeline automation for automated testing and evaluation, such as ArgoCD, Tekton, Gitlab CI/CD
• Experience building software prototypes and evaluation harnesses from first concept, maturing through the production lifecycle
• Knowledge of common machine learning frameworks, such as Tensorflow, Torch, Sci-kit Learn, and their associated testing/evaluation utilities
• Working knowledge of the NIST AI Risk Management Framework (AI RMF), MITRE ATLAS, OWASP ML/LLM Top 10, and related AI assurance and governance standards
• Knowledge of enterprise-scale machine learning operations environments, including GPU Computing and High-Performance Computing (HPC), for large-scale evaluation workloads
• Knowledge of AI/ML orchestration frameworks such as Kubeflow, Flyte, for orchestrating evaluation and validation workflows
• Familiarity with cloud computing services, such as AWS, GCP, Azure
• Familiarity with AI observability, monitoring, and telemetry tools such as Langfuse, Splunk, Prometheus, Grafana, Dynatrace, and Sysdig to support ongoing verification of deployed models